Auxiliary Networks for Joint Speaker Adaptation and Speaker Change Detection

Speaker adaptation and speaker change detection have both been studied extensively to improve automatic speech recognition (ASR). In many cases, these two problems are investigated separately: speaker change detection is implemented first to obtain single-speaker regions, and speaker adaptation is then performed using the derived speaker segments for improved ASR. However, in an online setting, we want to achieve both goals in a single pass. In this study, we propose a neural network architecture that learns a speaker embedding from which it can perform both speaker adaptation for ASR and speaker change detection. The proposed speaker embedding is computed using self-attention based on an auxiliary network attached to a main ASR network. ASR adaptation is then performed by subtracting, from the main network activations, a segment dependent affine transformation of the learned speaker embedding. In experiments on a broadcast news dataset and the Switchboard conversational dataset, we test our system on utterances with a change point in them and show that the proposed method achieves significantly better performance as compared to the unadapted main network (10-14% relative reduction in word error rate (WER)). The proposed architecture also outperforms three different speaker segmentation methods followed by ASR (around 10% relative reduction in WER).